Objective Accurate acquisition of tree growth parameters is a necessary prerequisite for obtaining forest information and phenotypic characteristics and is of great significance in forest character assessment, forest carbon sink measurement, and optimization of forest management strategies. Nowadays, accurate extraction of tree growth parameters from light detection and ranging data and prediction of tree growth parameters in the future will provide technical support for the digital development of forestry.
Method In this study, an artificial intelligence based approach to tree growth parameter extraction and prediction was proposed. Four tree species in Nanjing Forestry University, i.e., sakura, ginkgo, liriodendron, and Chinese fir, were selected as experimental objects. First, airborne laser scanning was used to obtain the point cloud data of four tree species sample plots, and the individual tree segmentation algorithm was used to obtain a single tree point cloud. Second, growth parameters such as DBH height, tree height, and crown width were automatically extracted from the single tree point cloud using the circle fitting and Gaussian filter algorithms, and supplemented with artificial in-situ measurement data (measured in 2015, 2017, 2019, 2021, and 2022). The time series of single tree growth parameters of different tree species were constructed as the training sample set for the deep learning network. Finally, a deep learning network for predicting tree growth parameters was constructed with a two-layer Gated Recurrent Unit (GRU) network, and an attention module was introduced to compensate for the shortcomings of traditional recurrent neural networks in capturing long-term dependencies. The network took the time series of individual tree growth parameters from 2015 to 2021 as the input, relied on training data and stochastic gradient descent algorithm to approximate the network parameters to the real tree growth situation, and was used to predict the growth parameters of individual trees in 2022.
Result The network performed best in the prediction of tree height, with the R2 not less than 0.83 and root mean square error (ERMS) less than 0.50 m. The prediction results of the ginkgo tree were the best among the four species (R2 = 0.95, ERMS = 0.31 m). The depth prediction network still had an acceptable performance in the prediction of parameters such as DBH height and crown width, R2 was not less than 0.81, DBH height ERMS was less than 2.50 cm, crown width ERMS was less than 0.32 m. The model in this paper predicted well (R2 ≥ 0.86) with less error compared with other tree parameter prediction methods such as linear regression and LSTM networks.
Conclusion The cascaded recurrent neural network can effectively predict the future tree growth and improve the prediction accuracy of tree growth parameters, while the introduction of GRU and attention model has certain robustness in the time-series prediction of growth parameters, which provides a new idea for the intelligent management and visualization analysis of forests.